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Activity Number:
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245
- SLDS CSpeed 4
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Type:
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Contributed
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Date/Time:
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Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #317871
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Title:
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The Bethe Hessian and Information Theoretic Approaches for Online Change-Point Detection in Network Data
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Author(s):
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Jiarui Xu* and Neil Hwang and Sharmodeep Bhattacharyya and Dr. Shirshendu Chatterjee
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Companies:
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Oregon State University and City University of New York - Bronx Community College and Oregon State University and City University of New York
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Keywords:
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Change-point detection;
Bethe Hessian Operator;
Spectral clustering;
Community detection;
Sparse networks;
Variation of information
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Abstract:
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Sequences of networks are currently a common form of network data sets. Identification of structural change-points in a network data sequence is a natural problem. The problem of change-point detection can be classified into two main types - offline change-point detection and online or sequential change-point detection. In this paper, we propose three different algorithms for online change-point detection based on certain cusum statistics for network data with community structures. For two of the proposed algorithms, we use information-theoretic measures to construct the statistic for the estimation of a change-point. In the third algorithm, we use eigenvalues of the Bethe Hessian matrix to construct the statistic for the estimation of a change-point. We show the consistency property of the estimated change-point theoretically under networks generated from the multi-layer stochastic block model and the multi-layer degree-corrected block model. We also conduct an extensive simulation study to demonstrate the key properties of the algorithms as well as their efficacy.
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Authors who are presenting talks have a * after their name.
